import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
import tensorflow as tf

class NN(object):
    
    def __init__(self):
        self.df = pd.read_csv('./NN/scenic_data.csv')

        def create_dataset(self, data,n_strps):
            """构建数据
            """
        x, y = [], []
        for i in range(len(data) - n_strps):
            x.append(data[i:i+n_strps])
            y.append(data[i+n_strps, :18])
        return np.array(x), np.array(y)
    
    def get_model(self):
        n_steps = 7  # 长度七天
        data = self.df.values
        X, y = self.create_dataset(data, n_steps)


train_size = int(len(X) * 0.8)
X_train, X_test = X[:train_size], X[train_size:]
y_train, y_test = y[:train_size], y[train_size:]

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.LSTM(50, activation='relu', return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(tf.keras.layers.LSTM(50, activation='relu'))
model.add(tf.keras.layers.Dense(18))
model.compile(optimizer='adam', loss='mse')
model.fit(X_train, y_train, epochs=50, validation_data=(X_test, y_test))

# 模型评估
loss = model.evaluate(X_test, y_test)
print(f"测试集损失: {loss}")





    